{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T15:23:31Z","timestamp":1792682611234,"version":"4.5.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Neuroradiol J"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Meningioma is the commonest primary brain tumour. Volumetric post-contrast magnetic resonance imaging (MRI) is recognised as gold standard for delineation of meningioma volume but is hindered by manual processing times. We aimed to investigate the utility of a model-based variational approach in segmenting meningioma.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A database of patients with a meningioma (2007\u20132015) was queried for patients with a contrast-enhanced volumetric MRI, who had consented to a research tissue biobank. Manual segmentation by a neuroradiologist was performed and results were compared to the mathematical model, using a battery of tests including the S\u00f8rensen\u2013Dice coefficient (DICE) and JACCARD index. A publicly available meningioma dataset (708 segmented T1 contrast-enhanced slices) was also used to test the reliability of the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      49 meningioma cases were included. The most common meningioma location was convexity ( n = 15, 30.6%). The mathematical model segmented all but one incidental meningioma, which failed due to the lack of contrast uptake. The median meningioma volume by manual segmentation was 19.0\u00a0cm\n                      <jats:sup>3<\/jats:sup>\n                      (IQR 4.9\u201331.2). The median meningioma volume using the mathematical model was 16.9\u00a0cm\n                      <jats:sup>3<\/jats:sup>\n                      (IQR 4.6\u201328.34). The mean DICE score was 0.90 (SD = 0.04). The mean JACCARD index was 0.82 (SD = 0.07). For the publicly available dataset, the mean DICE and JACCARD scores were 0.90 (SD = 0.06) and 0.82 (SD = 0.10), respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Segmentation of meningioma volume using the proposed mathematical model was possible with accurate results. Application of this model on contrast-enhanced volumetric imaging may help reduce work burden on neuroradiologists with the increasing number in meningioma diagnoses.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19714009231224442","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T07:59:48Z","timestamp":1703577588000},"page":"199-205","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A semi-automatic segmentation method for meningioma developed using a variational approach model"],"prefix":"10.1177","volume":"37","author":[{"given":"Liam","family":"Burrows","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences and Centre for Mathematical Imaging Techniques, University of Liverpool, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jay","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of 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